For these reasons, we propose to adapt a learning-to-rank framework that optimizes multiple parameters of phrase-based retrieval models effectively with less computation cost and without any specific retrieval metric.

we use a gradient descent method with the RankNet cost function (Burges et al., 2005) to perform effective parameter optimizations, as in (Taylor et al., 2006;Metzler, 2007).


As shown in the table, the multi-parameter model improves by approximately 18% and 12% on the TREC-6 and 7 partial query sets, and it also significantly outperforms both the word model and the one-parameter model on the TREC-8 query set.

the improvement on the TREC-8 query set shows one advantage of using our proposed method; by separating potentiallyineffective phrases and effective phrases based on the features, it not only improves the retrieval performance for each query but makes parameter learning less sensitive to the training set.


(iv) Which of the collection, the query, or the document should the selection of an expansion term be dependent on?

what are the strongest simplifications in terms of conditional independencies between variables that can be assumed, without hurting performance?


The fundamental difference between EEM1, EEM2, EEM3 on the one hand and EEM4 on the other is that EEM4 assumes independence between c and Q (thus P (c|Q) is set to P (c)).

the importance of the external collection is independent of the query.


First of all, we observe behavior typical for all query expansion methods: some topics are helped, some are not affected, and some are hurt by the use of EEM1; see Figure 1, top row.

27 topics show a slight drop in AP (maximum drop is 0.043 AP), 3 topics do not change (as no expansion terms are identified) and the remainder of the topics (120) improve in AP.


Investigating a held-out development set of queries retrieved from the United States web site (www.yahoo.com), we empirically decided the following values for the parameters, W = 1, F = 50, and U = 5.

we only accepted the queries for which the contents of the urls agree on the same language, that are submitted less than 50 times, and at least have 5 unique urls clicked.


From these counts, we obtained a probability distribution for all the words in our vocabulary.

this time we used a word-based ngram method, only with n = 1.


Furthermore, the performance of the system increases with the increasing size of training data.

the improvement that we get for Spanish, French, and German queries are strikingly good.


We find that our bilingual rankings have good monolingual ranking properties.

given an optimal pairwise bilingual ranking, we show that simple heuristics can effectively approximate the optimal monolingual ranking.


As Figure 1 shows as the number of times a query is issued increases, so does the chance of it being bilingual.

nearly 45% of the highest-frequency English queries and 35% of the highest-frequency Chinese queries are bilingual.


Among which, 7.2% involved rarely used character, and 98.4% were assigned common classifications of their causes by human subjects.

80% of the errors observed in writings of middle school students were related to the pronunciations and 30% were related to the compositions of words.


Tree substition grammars (TSGs) have potential advantages over regular context-free grammars (CFGs), but there is no obvious way to learn these grammars.

learning procedures are not able to take direct advantage of manually annotated corpora like the Penn Treebank, which are not marked for derivations and thus assume a standard CFG.


In this case, we use ten morphemes to determine whether or not given two morphemes have a dependency relation.

to decide the relation of morphemes j and i (j < i), we use morphemes j−2, j−1, j, j+1, j+2 and i−2, i−1, i, i+1, i+2.


This contrasts with Open Information Extraction (Banko and Etzioni, 2008) and On-Demand Information Extraction (Sekine, 2006), which aim to extract large databases of open-ended facts, and with supervised relation extraction, which requires additional supervised data to learn new relations.


Many of our differences with Carlson and Marcu (2001), who defined EDUs for the RST Discourse Treebank (Carlson et al., 2002), are due to the fact that we adhere closer to the original RST proposals (Mann and Thompson, 1988), which defined as 'spans' adjunct clauses, rather than complement (subject and object) clauses.

we propose that complements of attributive and cognitive verbs (He said (that)..., I think (that)...) are not EDUs.


Thus, (Raux and Eskenazi, 2004) conducted a study on adaptation of spoken dialog systems to non-native users.

dB-CALL systems should obviously deal with grammar errors because language learners naturally commit numerous grammar errors.


We used the learning and inference algorithms provided in the open-source Alchemy package (Kok et al., 2006).

we performed inference using the belief propagation algorithm (Pearl, 1988), and generative weight learning.


In this paper, we use maximum entropy models normalized for each predicate to each case.

the identifier directly selects the best phrase that   where n, c, and v denote a noun phrase of an argument, the case, and the target predicate, respectively, N denotes a set of candidate arguments, d(n) is a function that returns 1 iff the phrase n becomes the argument, and M c denotes the model of the case c. In addition, f k (d(n j ) = 1, X j ) is a feature function, λ c k denotes a weight parameter of the feature function, and A denotes an article in which all sentences are parsed.


Because the appearance order of argument phrases is not strongly constricted in Japanese, we construct generation models that reflect dependency relations among a predicate, its case and a noun phrase.

we regard generation probabilities P (n|c, v) acquired from the dependency tree as the scores of language models.


To account for temporal changes in barge-in rates, we set a window when calculating them (Komatani et al., 2008).

when the window width is N , the rates are calculated by using only the last N utterances, and the previous utterances are discarded.


The term 'query-focus' is less precisely defined, but is related to the cognitive task of focusing a summary on the query, which we assume humans do naturally.

the human generated model summaries are assumed to be queryfocused.


Also, on the negative sample set, we found that 69.12% sentences were query-biased.

on an average, 76.67% of the sentences picked by any automated summarizer are query-biased.


This clearly indicates that there is a strong negative correlation between likelihood of occurrence of a non-query-term and ROUGE-2 score.

a strong positive correlation between likelihood of occur-rence of a query-term and ROUGE-2 score.


Somers (2005) therefore states: "To be really sure of our results, we should like to replicate the experiments evaluating the translations using a more old-fashioned method involving human ratings of intelligibility."

apparently nobody has ever seriously compared backtranslation scores to human judgments, so the belief about their inutility seems not sufficiently backed by facts.


Using the OrthoBLEU algorithm, the evaluations previously conducted with the NIST tool were repeated.

both the Babel Fish translations as well as their back-translations were evaluated, whereby in the first case the human translations and in the second case the source sentences served as references.


However, this does not prove to be a problem, as we process each comma independently.

we only split the dividable part of this kind of sentences, leaving the non-dividable part unchanged.


In the case where two intermediate nodes share the same intermediate rule anchored to the same forest nodes, they can be shared.

we need only generate one k-best list of derivations, then use it in derivations rooted by both rules.


Therefore we suggest to use HMTM and tree-modified Viterbi algorithm for tasks interpretable as labeling nodes of dependency trees.

we show that the transfer phase in a Machine Translation system based on tectogrammatical dependency trees can be seen as a task suitable for HMTM.


(3) The source-language tree and the target-language tree are required to be isomorphic.

only node labeling can be changed in the transfer step.


So, one solution to this unbalanced class distribution is to split the 'nonemotion' (emo_ntrl) class into several subclasses.

given a POS tagset POS, we generate new emotion classes, 'emo_ntrl-C'|CPOS.


Keyword searching process can detect most of comparative sentences (S1, S2 and S3) 3 from original text documents.

the recall is high but the precision is low.


Similarly to spam filtering and sentiment classification, a key question asked in this research is whether it is possible to perform the task on the basis of simple lexical features of various types.

is it possible to automatically detect satire without access to the complex inferencing and real-world knowledge that humans make use of.


However, if the overall sentiment is computed by considering only the number of semantically identical phrases in the reviews, it misses the big picture.

when opinions regarding attributes are described with respect to expressions whose polarities are dependent on the specific contexts such as the weather or user preference, an overestimated or underestimated weight of the sentiment for each attribute may be assigned.


Following this method, the ViDic is constructed so that the set of entries in the ViDic consists of all strings that denote a concept.

if c is a concept, its key phrases, its title name, its redirect name and its category are all added as entries in the ViDic.


Both methods will complement each other to increase the expected performance of the model.

the method has the following steps: • Step 1: We propose a method that exploits specific characteristics of Vietnamese (Chau and Tuoi, 2007).


• Step 2: In case the sentence has no meaning word among phrases, the key phrase extracting process is based on the ViO ontology via concept matching.

this step has the following phases: 1. every candidate phrase in the sentence is matched to an entry in the VicDic dictionary especially when new phrases are not a concern or do not exist in the dictionary.


However, we do not use the whole formula.

, we use a similar formula that is specified as follows: Acu_Sim(w, c) = Sim(w, c) + Sim(w, c') in which, w is the phrase that needs to be annotated, c is the candidate concept and c' is the concept that is related to c. At the current node c while traversing, the similarity values between t and all children of c are calculated.


Furthermore, we had to construct all necessary linguistic resources and define all data structures from scratch, while enjoying some advantages derived from the many existent methodologies for morpho-syntactic annotation and the high consciousness of a standardization tendency.

we built a set with 434 noun phrase patterns and a rules set for Vietnamese key phrase identification.


In this paper, we present a possible method of bridging the morpho-syntactic gap for English-Korean SMT.

the proposed method tries to transform a source sentence by inserting pseudo words, and by reordering the sentence in such a way that both sentences have a similar length and word order.


In this paper, we present a preprocessing method for both training and decoding in English-Korean SMT.

we transform a source language sentence by inserting pseudo words and syntactically reordering it to form a target sentence structure in hopes of reducing the morphosyntactic discrepancies between two languages.


However, in practice, this pruning scheme does not work well with phrase tables, as many phrase pairs receive the same significance values.

many phrase pairs in the phrase table have joint and both marginal frequencies all equal to 1.


However, it has been shown in (Moore, 2004) that when a large number of singleton-singleton pairs, such as triple-1 phrase pairs, are observed, most of them are not due to chance.

most triple-1 phrase pairs are significant and it is likely that the translation quality will decline if all of them are discarded.


Semantic DSN: The construction of this network is inspired by (Lin, 1998).

we parsed a dump of English Wikipedia (July 2008) with the XLE parser (Riezler et al., 2002) and extracted the following dependency relations for nouns: Verb-Subject, Verb-Object, Nouncoordination, NN-compound, Adj-Mod.


On the other hand, for the syntactic DSN, the first 10 eigenvalues cover 75% of the spectrum while the first 20 covers 80%.

the structure of the syntactic DSN is governed by a few (order of 10) significant principles, whereas that of the semantic DSN is controlled by a large number of equally insignificant factors.


Moreover, the basic algorithm presented here leaves a lot of opportunities for future improvement.

our algorithm does not constraint the scoring function in opposite to Viterbi based decoders.


A paraphrase generation tool usually starts with a sentence which may be very similar to some potential solution.

there is no need to "translate" all of the sentences.


For the same reasons, this algorithm sounds interesting for paraphrase generation.

it does not put constraint on the scoring function.


Note that the model predicts the rank of a i and a j through calculating S T H (a i , a j , r) rather than S T H (a j , a i , r), where a i precedes a j .

the position information is implicitly encoded in the model rather than explicitly as a feature.


Experiments are conducted using data from the TDT-2 Voice of America Mandarin broadcast.

we only use the data from the long programs (40 programs, 1458 stories in total), each of which is about one hour in duration.


A template of part-of-speech tag features was added for CoNLL00, CoNLL03, and Encyclopedia.

all tasks except PTB and NetTalk require assigning a label to a phrase rather than to a word; hence, we used standard "BIO" encoding.


If the t-th component is selected for update, a sequential IS method solves the following one-variable sub-problem: and (5) An approximate function A t (z) satisfying (4) does not ensure that the function value is strictly decreasing.

the new function value L(w + ze t ) may be only the same as L(w).


The last condition shows thatA(z)is separable, so min z A(z) = t min zt A t (z t ).

,we can minimizeA t (z t ),∀t simultaneously, and then update w t ∀t together.


The sequence {w k } generated by any of these four methods linearly converges.

there is a constant µ ∈ where w * is the global optimum of (3).


In this algorithm each particle can learn from the experience of other particles in the same population (called swarm).

each particle in the iterative search process would adjust its flying velocity as well as position not only based on its own acquaintance but also other particles' flying experience in the swarm.


Inference in MRTFs is somewhat complicated from inference in LDA, due to the introduction of the additional potential functions.

while it is possible to analytically integrate out θ in LDA (due to multinomial/Dirichlet conjugacy), this is no longer possible in MRTFs.


The acceptance probability then just depends on the graph distances.

once θ d is drawn from the posterior Dirichlet, the acceptance probability becomes d ∈N  document, we run 10 Metropolis steps; the acceptance rates are roughly 25%.


In this paper, we have argued that carrying out taskbased evaluations of NLG systems over the Internet is a valid alternative to more traditional laboratorybased evaluations.

we have shown that an Internet-based evaluation of systems in the GIVE Challenge finds consistent significant differences as a lab-based evaluation.


In this paper, we explore the applicability of computational approaches to the recognition of deceptive language.

we investigate whether automatic classification techniques represent a viable approach to distinguish between truth and lies as expressed in written text.


Although acoustic and other non-linguistic features were also found to be useful for this task (Hirschberg et al., 2005), we deliberately focus on written language, since it represents the type of data most frequently encountered on the Web (e.g., chats, forums) or in other collections of documents.

we try to answer the following two questions.


The main goal of this paper is to investigate the potential of stylistic features in improving the performance of learning-based mobile spam filters.

we adopt stylistic features previously suggested in authorship attribution studies based on stylometry, the statistical analysis of linguistic style.


This work addresses domain portable Question Answering (QA) over Topic Maps.

a QA system capable of retrieving answers to a question asked against one particular topic map or topic maps collection at a time.


We consider that AP takes precedence over the EAT.

if the AP (the explicit focus) has been successfully identified in the question, it is considered to be the type of the question, and the EAT (the implicit focus) is left aside.


Given N the total number of questions,    we define the branching factor, that is, the percentage of questions for which AP is provided by the system, as follows: Figure 1 shows that the sum AP true positives and EAT correct classifications represents the overall number of questions that were classified correctly.

this accuracy can be further developed to present the dependencies as follows: the overall accuracy is dependent on the precision of the AskingPoint class of the AP classifier, the accuracy of EAt and the branching factor.


The specificity of the focus degrades gracefully in the approach described above.

we attempt the extraction of the AP when possible and fall back on the EAT extraction otherwise.


The basic idea comes from encoding each integer by the smallest number of required bytes.

an integer within the range of 0 to 255 is coded as a 1-byte integer, the integers within the range of 256 to 65,535 are stored as 2-byte integers, and so on.


Statistical language models are potentially useful for any language technology task that produces natural-language text as a final (or intermediate) output.

they are extensively used in speech recognition and machine translation.


Co-training is one of those methods, and has been extensively studied in NLP (Nigam and Ghani, 2000;Pierce and Cardie, 2001;Ng and Cardie, 2003;Mota and Grishman, 2008).

we showed that the performance of a name tagger based on co-training decays as the time gap between training data (seeds and unlabeled data) and test data increases (Mota and Grishman, 2008).


Two main results can be highlighted: (i) The best overall refinement is, in average, very good (F = .89 for clusters of size ≥ 3).

there is usually at least one QR that leads to (approximately) the desired set of results; (ii) this best refinement, however, is not necessarily an intuitive choice for the user.


A common strategy, and one which forms the focus of this paper, is to factor each dependency tree into small parts, which can be scored in isolation.

factored parsing can be formalized as follows: we treat the dependency tree y as a set of parts p, each of which makes a separate contribution to the score of y.


We define the order of a part according to the number of dependencies it contains, with analogous terminology for factorizations and parsing algorithms.

in the remainder of this paper, we focus on factorizations utilizing the following parts: Sections 4.1, 4.2, and 4.3 describe parsers that, respectively, factor trees into grandchild parts, grand-sibling parts, and a mixture of grand-sibling and tri-sibling parts.


As remarked by Eisner (1996) and McDonald and Pereira (2006), it is possible to rearrange the dynamic-programming structures to conform to an improved factorization that decomposes each tree into sibling parts-pairs of dependencies with a shared head.

a sibling part consists of a triple of indices (h, m, s) where (h, m) and (h, s) are dependencies, and where s and m are successive modifiers to the same side of h. In order to parse this factorization, the secondorder parser introduces a third type of dynamicprogramming structure: sibling spans, which represent the region between successive modifiers of some head.


The first parser, Model 0, factors each dependency tree into a set of grandchild parts-pairs of dependencies connected head-to-tail.

a grandchild part is a triple of indices (g, h, m) where (g, h) and (h, m) are dependencies.


Model 1 decomposes each tree into a set of grand-sibling parts-combinations of sibling parts and grandchild parts.

a grand-sibling is a 4-tuple of indices (g, h, m, s) where (h, m, s) is a sibling part and (g, h, m) and (g, h, s) are grandchild parts.


For example, we extract subtree "社 会(society):2-边缘(fringe):0" on the Chinese side and get its corresponding subtree "fringes(W 2):0of:1-society(W 1):2" on the English side, where W 1 means that the target word is aligned to the first word of the source subtree, and W 2 means that the target word is aligned to the second word of the source subtree.

we have a sub-tree pair: "社 会(society):2-边 缘(fringe):0" and "fringe(W 2):0-of:1-society(W 1):2".


Note that the features based on 3to2 and 3to3 can not be applied to the first-order models, because they only consider single dependencies (bigram).

in the first model, F BI only includes the features based on 2to2 and 2to3.


While the mean number of scope readings in the subcorpus is in the millions, our system computes on average 4.5 weakest readings for each sentence, in less than twenty milliseconds; over 80% of all sentences are reduced to at most two weakest readings.

we make it feasible for the first time to build an application that uses the individual (weakest) semantic representations computed by the ERG, both in terms of the remaining ambiguity and in terms of performance.


Regular tree languages enjoy many of the closure properties of regular string languages.

we will later exploit that RTLs are closed under intersection and complement.


The rule then rewrites t as described above.

annotated rewrite systems are rewrite systems where rule applications are restricted to subtrees with specific annotations.


We define a configuration t of a dominance graph D to be a R-relative normal form of (the configurations of) D iff there is no other configuration t of D such that t → R t .

these are the configurations that can't be weakened further without obtaining a tree that is no longer a configuration of D. if R approximates entailment, then the R-relative normal forms approximate the weakest readings.


However, it can be shown that if the rules in R have at most depth two and G D is deterministic, then the entire above construction can be computed in time O(|G D | • |R|) (Koller and Thater, 2010).

we have shown how to compute the weakest readings of a hypernormally connected dominance graph D, as approximated by a weakening rewrite system R, in time linear in the size of G D and linear in the size of R. This is a dramatic improvement over the best previous algorithm, which was quadratic in |conf(D)|.


Every tree t in conf(D) that is not in L(G ) is equivalent to some tree t in L(G ), because if t could not be rewritten into such a t , then t would be in relative normal form.

the algorithm solves the redundancy elimination problem.


1 This remains true on the entire Rondane corpus (although the reduction factor is lower because we have no rules for the rare scope-bearers): RE+WR computation takes 32 seconds, compared to 30 seconds for RE.

our algorithm brings the semantic ambiguity in the Rondane Treebank down to practically useful levels at a mean runtime investment of a few milliseconds per sentence.


Therefore, their identification is important for the automatic construction of knowledge bases.

the distinction between generic and non-generic statements is crucial for the correct encoding of generic and instance-level information.


Although our results are satisfying, in future work we will extend the range of features for further improvements.

we will address lexical semantic features, as they tend to be effected by sparsity.


The key point of our method is a reliable semantic relatedness measure between concepts (including WordNet concepts, NEs and Wikipedia concepts), called Structural Semantic Relatedness, which can capture both the explicit semantic relations between concepts and the implicit semantic knowledge embedded in graphs and networks.

we first extract the semantic relations between two concepts from a variety of knowledge sources and Machine learning represent them using a graph-based model, semantic-graph.


In this step we represent the semantic relations as the edges between nodes.

for each pair of extracted concepts, we identify whether there are semantic relations between them: 1) If there is only one semantic relation between them, we connect these two concepts with an edge, where the edge weight is the strength of the semantic relation; 2) If there is more than one semantic relations between them, we choose the most reliable semantic relation, i.e., we choose the semantic relation in the knowledge sources according to the order of WordNet, Wikipedia and NE Co-concurrence corpus (Suchanek et al., 2007).


In this paper we demonstrate how to enhance the named entity disambiguation by capturing and exploiting the semantic knowledge existed in multiple knowledge sources.

we propose a semantic relatedness measure, Structural Semantic Relatedness, which can capture both the explicit semantic relations and the implicit structural semantic knowledge.


These external simulation models are different from internal user models used by the dialogue system.

our model is the first to be sensitive to a system's choices of referring expressions.


The other potential problem is the socalled "bag-of-sentences" assumption implicitly made by most of these summarizers.

sentences are classified independently of each other, without leveraging the dependence relationships among the sentences or the global structure of the document (Shen et al., 2007).


If the output (classification) score belongs to the positive class, i S will be selected as part of the summary; otherwise, it will be excluded (Kupiec et al., 1999).

the problem can be formulated as follows: Construct a sentence ranking model that assigns a classification score (or a posterior probability) of being in the summary class to each sentence of a spoken document to be summarized; important sentences are subsequently ranked and selected according to these scores.


These features can be integrated in a systematic way into the proposed framework by taking the advantage of the learning capability of the supervised machine-learning methods.

the prior probability   j S P can be approximated by: where   are the likelihoods that a sentence j S with features j X are generated by the summary class S and the nonsummary class S , respectively; the prior probability   , several popular supervised classifiers (or summarizers), like BC or SVM, can be leveraged for this purpose.


However, if the language under consideration is anything other than English, then a translation into English (or some other reference language) is for most purposes a perfectly adequate meaning representation.

we view machine translation as an approximation to language understanding.


The context vector of a word w collects words and maps them to some score of their "relatedness" to w; in this case, we used PMI.

nAS, in contrast, relies on the signature, the set of n words most related to w. it requires a Boolean relatedness indication, rather than a numeric relatedness score.


This paper presents WOE (Wikipedia-based Open Extractor), the first system that autonomously transfers knowledge from random editors' effort of collaboratively editing Wikipedia to train an open information extractor.

wOE generates relation-specific training examples by matching Infobox 1 attribute values to corresponding sentences (as done in Kylin (wu and weld, 2007) and Luchs (Hoffmann et al., 2010)), but wOE abstracts these examples to relationindependent training data to learn an unlexicalized extractor, akin to that of TextRunner.


Take We call such a generalized-corePath an extraction pattern.

in total, WOE builds a database (named DB p ) of 15,333 distinct patterns and each pattern p is associated with a frequency -the number of matching sentences containing p. 185 patterns have f p ≥ 100 and 1929 patterns have f p ≥ 5.


We use the same matching sentence set behind DB p to generate positive examples for WOE pos .

for each matching sentence, we label the subject and infobox attribute value as arg 1 and arg 2 to serve as the ends of a linear CRF chain.


In this section, we compare the overall performance of WOE parse , WOE pos and TextRunner (shared by the Turing Center at the University of Washington).

we are going to answer the following questions: 1) How do these systems perform against each other?


This is because the parser features help to handle complicated and longdistance relations in difficult sentences.

wOE parse outputs 1.42 triples per sentence on average, while wOE pos outputs 1.05 and TextRunner outputs 0.75.


Note that we measure TextRunner's precision & recall differently than (Banko et al., 2007) did.

we compute the precision & recall based on all extractions, while Banko et al.


To compare with TextRunner, we tested four different ways to generate training examples from Wikipedia for learning a CRF extractor.

positive and/or negative examples are selected by TextRunner's hand-written rules (tr for short), by WOE's heuristic of matching sentences with infoboxes (w for short), or randomly (r for short).


We use CRF +h 1 −h 2 to denote a particular approach, where "+" means positive samples, "-" means negative samples, and h i ∈ {tr, w, r}.

"+w" results in 221,205 positive examples based on the matching sentence set 6 .


We formally demonstrate the superiority of AQL and SystemT in terms of both expressivity and efficiency (Section 4).

we show that 1) the expressivity of AQL is a strict superset of CPSL grammars not using external functions and 2) the search space explored by the SystemT optimizer includes operator graphs corresponding to efficient finite state transducer implementations.


The class of extraction tasks expressible as AQL queries is a strict superset of that expressible through expanded code-free CPSL grammars.

(a) Every expanded code-free CPSL grammar can be expressed as an UnambigEquiv AQL query.


The question arises as to whether there are other annotators for which the traditional transducer approach is superior.

for a given annotator, might there exist a finite state transducer that is combinatorially faster than any possible algebra graph?


Table 3 shows that T-NE is at least an order of magnitude faster than ANNIE-Optimized across all datasets.

on Finance L T-NE's throughput remains high, whereas the performance of both ANNIE and ANNIE-Optimized degraded significantly.


A similar trend can be observed on the other datasets as shown in Table 3.

for Finance L , both ANNIE and ANNIE-Optimized required 8GB of Java heap size to achieve reasonable throughput 7 , in contrast to T-NE which utilized at most 300MB out of the 2GB of maximum Java heap size allocation.


We assigned undirected edges between vertices that represent adjacency in quoted speech fragments.

we set the weight of each undirected edge between two character vertices to the total length, in words, of all quotes that either character speaks from among all pairs of adjacent quotes in which they both speak-implying face to face conversation.


The average degree of the graph, defined as where |E v | is the number of edges incident on a vertex v, and |V | is the number of vertices.

this determines the average number of characters connected to each character in the conversational network ("with how many people on average does a character converse?").


This allowed us to take a systematic and wide look at a large corpus of texts, an approach which complements the narrower and deeper analysis performed by literary scholars and can provide evidence for or against some of their claims.

we described a high-precision method for detecting face-to-face conversations between two named characters in a novel, and showed that as the number of characters in a novel grows, so too do the cohesion, interconnectedness and balance of their social network.


These fire for for each link (e, f ) and partof-speech tag.

we learn a separate weight for each feature for each part-of-speech tag in our data.


The reply contains the unit of measure (here "year-old") and the numerical answer.


The IDTB system handles this situation by using a high bid, allowing the user to outbid the system as its contribution is more important.

the IDTB user can win the turn when it should have it, but the KR user cannot.


Because German distinguishes more grammatical roles morphologically than English, we experiment with various granularities of role labelling.

subj/obj distinguishes the subject position, the object position, and another category for all other positions.


The final ordering is achieved by first randomizing the order of the constituents in a clause (besides the first one, which is selected to be the VF), then sorting them according to the precedence probabilities.

a constituent A is put before a constituent B if MAXENT2(A,B) > 0.5.


We incorporate local coherence information into the model by adding entity transition features which we found to be useful in the sentence ordering experiment in Section 3 above.

we add features indicating the topological fields in which entities occur in the previous sentences.


Whereas in LSA words are represented as points in a multi-dimensional space, LDA represents words using topics.

each document in a corpus is modeled as a distribution over K topics, which are themselves characterized as distribution over words.


Data The models discussed in the previous section were evaluated against an eye-tracking corpus.

we used the English portion of the Dundee Corpus (Kennedy and Pynte 2005) which contains 20 texts taken from The Independent newspaper.


Secondly, the regression equation makes more sense on the log scale as the contribution of each term to raw reading time is multiplicative rather than additive.

log(t) = ∑ i β i x i implies t = ∏ i e β i x i .


That is, log(t) = ∑ i β i x i implies t = ∏ i e β i x i .

the intercept term for each participant now represents a multiplicative factor by which that participant is slower or faster.


There are also other transformations which can be made with no additional information.

sometimes the existing trees allow transformation rules to be written that improve the quality of the grammar.


For synsets, we calculate coverage as follows: where δ(S B , S F ) = 1 if the two synsets S B and S F have a synonym in common, 0 otherwise.

synset coverage is determined as the percentage of synsets of F that share a term with the corresponding babel synsets.


The similarity measure we use is based on the slot distributions of the arguments.

two arguments are considered similar if they tend to appear in the same slots.


In order to avoid tuning a parameter for each of the measures, we set the threshold as the median value of this measure in the test set.

we find the threshold which tags half of the arguments as cores and half as adjuncts.


It embodies the idea that a multitask learning architecture coupled with semi-supervised learning can be effectively applied even to complex linguistic tasks such as SRL.

(Collobert and Weston, 2008) proposes an embedding of lexical information using Wikipedia as source, and exploiting the resulting language model within the multitask learning process.


The aim of the evaluation is to measure the reachable accuracy of the simple model proposed and to compare its impact over in-domain and out-ofdomain semantic role labeling tasks.

we will evaluate the argument classification (AC) task in Section 4.2.


If a noun, n, depends on a word, w, with a relation, r, we collect a dependency pair, (n, 〈w, r〉).


Note that we do not deal with ambiguities in the construction of these sets as well as in the calculation of similarities.

a word can be contained in several sets, and the answers for such a word is the union of the words in the sets it belongs to (excluding the word itself).


We can see that the best α (= 0.0016) found for Set A works well for Set B as well.

the tuning of α as above is not unrealistic in MP@1 MP@5 MP@10 MP@20 MP@30 MP@40 Figure 2: Tuning of α (MP).


Therefore, even if BC b preferred lower-ID words than BC, it should not have the effect of improving the accuracy.

the improvement by BC b is not superficial.


With such a large scale, information in the blogosphere follows a Long Tail Distribution (Agarwal et al., 2010).

in aggregate, the not-so-well-known blogs can have more valuable information than the popular ones.


A prominent feature of the blogosphere is the collective wisdom (Agarwal et al., 2010).

the knowledge in the blogosphere is enriched by such engaging interactions among bloggers and readers as posting, commenting and tagging.


Essentially, it builds a topic profile for each original posting along with the comments from readers, and uses this profile to retrieve relevant articles.

we first extract structural, semantic, and authority information carried by the comments.


In this work, we use a graph model to differentiate the importance of each comment.

we model the authority, semantic, structural relations of comments to determine their combined impact.


In graph theoretic terms, the hierarchy can be represented as a tree = ( , ), where is the set of all text nodes and is the edge set.

the original posting is the root and all the comments are ordinary nodes.


The weight contribution from the comments { 1 , 2 , ⋅ ⋅ ⋅ , } incorporates not only the language features of these documents but also their importance in the discussion thread.

the contribution of comment score is incorporated into weight calculation of the words in a comment.


The origin = ⟨0, 0⟩ corresponds to the case where an article does contain neither 1 nor 2 .

corresponds to an article which can be interpreted as is said to be more general than , Δ = ( , ) should be greater than Δ = ( , ).


Such an interpretation provides a control on delivering recommended articles.

we can filter the duplicate articles to avoid recommending the same information.


In addition, we provide a significance test to show that the observed differences in effectiveness for different approaches are not incidental.

we use the -test here, which is commonly used for significance tests in information retrieval experiments (Hull, 1993).


In this part, we explore the contribution of user authority and comments in social media recommender.

we study the following scenarios with increasing system capabilities.


This is attributed by the text characteristics of these two forms of social media.

comments in news forums usually carry much richer structural information than blogs where comments are usually "flat" among themselves.


Results show that the error models learned from clickthrough data lead to significant improvements on the task of query spelling correction.

the speller system incorporating a phrase-based error model significantly outperforms its baseline systems.


Second, there must not be any word alignments from words inside the bi-phrase to words outside the bi-phrase.

we do not extract a phrase pair if there is an alignment from within the phrase pair to outside the phrase pair.


Therefore we only apply this heuristic to instances that are labeled with extremely high confidence (θ cf ≥ 2.5) and that pass a global sanity check, gsc(w) ≥ 0.2, which ensures that a relatively high proportion of labeled instances with the same head noun have been assigned to the same semantic class.

gsc(w) = 0.1 * where w l and w u are the # of labeled and unlabeled instances, respectively, w l/c is the # of instances labeled as c, and w u/c is the # of unlabeled instances that receive a positive confidence score for c when given to the classifier.


Relevancy can be computed by defining a similarity between lists using the vector-space model.

let L denote the corpus of lists, and P be the set of unique phrases from L. Each list l 0 ∈ L can be represented as a vector of weighted phrases p ∈ P appearing on the list, Following the notion of inverse document frequency, a phrase's weight is inversely proportional to the number of lists containing the phrase.


This paper shows that -with new techniques self-supervised learning of relation-specific extractors from Wikipedia infoboxes does scale.

we present LUCHS, a selfsupervised IE system capable of learning more than an order of magnitude more relation-specific extractors than previous systems.


In effect, OntoUSP conducts ontology induction, population, and knowledge extraction in a single integrated process.

given clusters c 1 , c 2 , in addition to merge vs. separate, On-toUSP evaluates a third option called abstraction, in which a new object cluster c is created, and ISA links are added from c i to c; the argument clusters in c are formed by merging that of c i 's.


Shrinkage takes place via interaction among the weights of the ISA mixture formula.

if the weights for some property cluster p are all zero, it means that values in p are completely predicted by p's ancestors.


(Thus , where p is the parent of c.) Like USP, OntoUSP imposes local normalization constraints that enable closed-form estimation of the optimal parameters and likelihood.

using m-estimation, the optimal w c,v is log((m • e w p,v + n c,v )/(m + n c )), where p is the parent of c and n is the count.


Therefore, we used the same methodology and dataset as the USP paper to evaluate OntoUSP on its capability in knowledge acquisition.

we applied OntoUSP to extract knowledge from the GE-NIA dataset (Kim et al., 2003) and answer questions, and we evaluated it on the number of extracted answers and accuracy.


An RgSST requires the root node of the given sub-tree to be part of the sub-structure.

all sub-structures should be generated from the root of the given sub-tree as presented in Fig.


To be fully consistent with the definition, we strictly reserve the semantic equivalence for the aligned sub-trees to keep a high precision.


This suggests that the structure correspondence cannot be used to measure the semantically equivalent tree structures alone, since the same syntactic structure tends to be reused in the same parse tree and lose the ability of disambiguation to some extent.

to capture the semantic similarity, structure features requires lexical features to cooperate.


First of all, it imposes a 1-to-1 constraint in word alignment.

a word is not allowed to align to more than one word.


It is found to be well handled by the K-Best parsing method in Huang and Chiang (2005).

during the bottom-up construction of the span pair repertoire, each span pair keeps only the best alignment hypothesis.


We will first evaluate pruning regarding the pruning decisions themselves.

the first evaluation metric, pruning error rate (henceforth PER), measures how many correct E-spans are discarded.


In contrast, string-to-tree translation (Galley et al., 2004;Galley et al., 2006;Chiang et al., 2009) is like bilingual parsing.

giving a (bilingual) translation grammar and a source sentence, we are trying to construct a parse forest in the target language.


Localizing these pointer-valued features is straightforward, since during parsing, the HEAD and SEM HEAD of a node are automatically transferred to its mother node.

the syntactic and semantic head of a node only take Algorithm 1 Fine-grained rule extraction Input: HPSG tree/forest E f , foreign sentence F , and alignment A Output: a PAS-based rule set R1 and/or a tree-rule set R2 1: if E f is an HPSG tree then 2: E ′ f = localize Tree(E f ) 3: , F , A) ◃ composed rule extraction algorithm in (Galley et al., 2006) 6: else if E f is an HPSG forest then 7: E ′ f = localize Forest(E f ); 8: R2 = forest based rule extraction(E ′ f , F , A) ◃ Algorithm 1 in  9: end if the identifier of the daughter node as the values.


For each leaf node n, we extract a minimum covering tree T c if n contains at least one argument.

at least one ARG⟨x⟩ takes the value of some node identifier, where x ranges 1 over 4 (line 3).


Recall the definition of minimum covering tree, which supports a faster way to retrieve available rules from PRS without generating all the subtrees.

when node n fortunately to be the root of some minimum covering tree(s), we use the tree(s) to seek available PAS-based rules in PRS.


For speed evaluation we held out three sets of sentences from each domain-specific corpus.

we used 30,000, 4,000 and 2,000 unique sentences of length 5-20, 21-40 and 41-250 tokens respectively.


As Table 5 shows, this training method produces models adapted to the new domain.

note that models trained on Wikipedia or the biomedical data produce lower F-scores 3 than the baseline on newswire.


When we use Sassano's algorithm, we have to generate training examples for the classifier by using the algorithm in Figure 4.

we should not sample bunsetsu pairs independently from a given sentence.


Let us consider the meaning of {j, i, "O"} if the head of the j-th bunsetsu is the k-th one such that j < k < i.

in the context of the algorithm in Figure 3, {j, i, "O"} actually means that the j-th bunsetsu modifies th l-th one such that i < l. "O" does not simply mean that two bunsetsus does not have a dependency relation.


We use the bar notation for sequences, sox means a sequence of variable length.

letx be a sequence of n letters and letȳ be a corresponding sequence of n tags.


In order to eliminate abbreviations and proper names which may not be English, we remove all words that are not fully lower-case.

we exclude words that contain capital letters, apostrophes, and/or periods.


These are learning experiments so we also use ten-fold cross validation in the same way as with CRF++.

we create a pattern file from 90% of the dataset using PATGEN, and then hyphenate the remaining 10% of the dataset using Liang's algorithm and the learned pattern file.


We do know that their training set was also derived from CELEX, and their maximum reported accuracy is slightly lower.

for English our word-level accuracy ("ower") is 96.33% while their best ("WA") is 95.65%.


Therefore, the second lessstandard experimental design for which we report results restricts attention to the most common English words.

we consider the top 4000 words that make up about three quarters of all word appearances in the American National Corpus, which consists of 18,300,430 words from written texts of all genres.


The training criterion is that n-grams that are present in the training corpus like x must have a score at least some margin higher than corrupted n-grams likẽ x.

: We minimize this loss stochastically over the n-grams in the corpus, doing gradient descent simultaneously over the neural network parameters and the embedding lookup table.


This delexicalization is performed separately from using the word representation.

if we have induced an embedding for 12/3/2008 , we will use the embedding of 12/3/2008 , and *DD*/*D*/*DDDD* in the baseline features listed above.


Thus we apply the ME classification algorithm for further combination and co-training.

we only employ Boolean features, representing the presence or absence of a word in a document.


Figure 5 shows the classification results of different methods with different sizes of the labeled data: 5%, 10%, and 15% of all data, where the testing data are kept the same (20% of all data).

the results of other methods including self-training, transductive SVM, and random views are presented when 10% labeled data are used in training.


Thus, topic models are a natural fit for modeling our relation data.

our system, called LDA-SP, uses LinkLDA (Erosheva et al., 2004), an extension of LDA that simultaneously models two sets of distributions for each topic.


Figure 2 illustrates the LinkLDA model in the plate notation, which is analogous to the model in (Erosheva et al., 2004).

note that each a i is drawn from a different hidden topic z i , however the z i 's are drawn from the same distribution θ r for a given relation r. To facilitate learn- ing related topic pairs between arguments we employ a sparse prior over the per-relation topic distributions.


By labeling this small number of topics we can infer class-based preferences for an arbitrary number of relations.

we applied a semi-automatic scheme to map topics to WordNet.


16 The transformations in the previous section do not perform any constituent level reordering, but rather eliminate certain English function words as tokens in the text and fold them into complex syntactic tags.

no transformations reorder the English SVO order to Turkish SOV, 17 for instance, or move postnominal prepositional phrase modifiers in English, to prenominal phrasal modifiers in Turkish.


More on Dominance Links Dominance links are quite common in tree description formalisms, where they were already in use in D-theory (Marcus et al., 1983) and in quasi-tree semantics for fb-TAGs (Vijay-Shanker, 1992).

d-tree substitution grammars are essentially the same as UVG-dls (Rambow et al., 2001), and quite a few other tree description formalisms subsume them (Candito and Kahane, 1998;Kallmeyer, 2001;Guillaume and Perrier, 2010).


It is well-known that CCG can generate languages that are not context-free (which is necessary to capture natural languages), but can still be parsed in polynomial time.

vijay-Shanker and Weir (1994) identified a version of CCG that is weakly equivalent to Tree Adjoining Grammar (TAG) (Joshi and Schabes, 1997) and other mildly context-sensitive grammar formalisms, and can generate non-context-free languages such as a n b n c n .


The discussion of the (weak and strong) generative capacity of CCG and TAG has recently been revived (Hockenmaier and Young, 2008;Koller and Kuhlmann, 2009).

koller and kuhlmann (2009) have shown that CCGs that are pure (i.e., they can only use generalized composition rules, and there is no way to restrict the instances of these rules that may be used) and first-order (i.e., all argument categories are atomic) can not generate a n b n c n .


Exclusions and restrictions of rules are also assumed in much of the language-theoretic work on CCG.

they are essential for the formalism used in the aforementioned equivalence proof for CCG and TAG (Vijay-Shanker and Weir, 1994).


The special property established by our transformation has consequences for the generative capacity of pure CCG.

we will now show that the set of all transformed derivations of a given grammar yields a context-free language.


The combinatory rules in B&K-CCG, given in Figure 4, are defined to be sensitive to the slash types.

slashes with the types˘and can only be eliminated by harmonic and crossed compositions, respectively.


This means that pure B&K-CCG is just as unable to generate L 3 as pure CCG is.

the weak generative capacity of CCG with rule restrictions, and in particular that of the formalism considered by Vijay-Shanker and Weir (1994), is strictly greater than the generative capacity of pure B&K-CCG-although we conjecture (but cannot prove) that pure B&K-CCG is still more expressive than pure non-modal CCG.


Given the heterogeneity of features in this class, we expect that they will provide reasonable accuracies for all the linguistic quality measures.

the overlap features might serve as a measure of redundancy and local coherence.


Word coherence can be considered as the analog of language models at the inter-sentence level.

we used the two features introduced by Soricut and Marcu (2006).


For each of the linguistic quality questions, the corresponding best class of features gives prediction accuracies around 90%.

if these features were used to fully automatically compare systems that participated in the 2007 DUC evaluation, only one out of ten comparisons would have been incorrect.


In this section we propose our methodology that enables us to identify the context information of a cited paper.

the task is to assign a binary label X C to each sentence S i from a paper S, where X C = 1 shows a context sentence related to a given cited paper, C. To solve this problem we propose a systematic way to model the network level relationship between consecutive sen-tences.


The belief of a node i, about its neighbor j to be in either classes is assumed to be 0.5 if i is in class 0.

if a node is not part of the context itself, we assume it has no effect on its neighbors' classes.


The second set of features that we are interested in are discourse-based features.

we match each sentence with specific patterns and flag those that match.


As the second baseline, we use the hand-crafted discourse based features used in MRF's potential function.

this baseline, B 2 , marks paper  Table 7: Average F β=3 for similarity based baseline (B 1 ), discourse-based baseline (B 2 ), a supervised method (SVM) and three MRF-based methods.


Here, we limit the context size to be 4 on each side.

we attach to a citing sentence any of its 4 preceding and following sentences if citation survey context survey QA CT nuggets 0.416 0.634 AB nuggets 0.397 0.594 DP CT nuggets 0.324 0.379 Table 9: Pyramid F β=3 scores of automatic surveys of QA and DP data.


Our approach therefore uses an ILP formulation which will provide a globally optimal solution, and which can be efficiently solved using standard optimization tools.

the model selects phrases from which to form the highlights, and each highlight is created from a single sentence through phrase deletion.


Constraints (1c) and (1d) enforce this requirement.

these constraints stop highlights formed from sentences at the beginning of the document (which tend to have high salience scores) from being too long.


Two annotators manually aligned the highlights and document sentences.

each sentence in the document was assigned one of three alignment labels: must be in the summary (1), could be in the summary (2), and is not in the summary (3).


Summarization In order to examine the generality of our model and compare with previous work, we also evaluated our system on a vanilla summarization task.

we used the same model (trained on the CNN corpus) to generate summaries for the DUC-2002 corpus 2 .


This attribute is presented if the topic relevant attribute has been labeled as none given or yes.

only the on-topic sentences are considered in this step.


However, in our annotation process annotators were tasked to identify only one entity as the referent, and was free to choose it from anywhere in the discourse.


In the area of media analysis, one of the key tasks is collecting detailed information about opinions and attitudes toward specific topics from various sources, both offline (traditional newspapers, archives) and online (news sites, blogs, forums).

media analysis concerns the following system task: given a topic and list of documents (discussing the topic), find all instances of attitudes toward the topic (e.g., positive/negative sentiments, or, if the topic is an organization or person, support/criticism of this entity).


E.g., in our background corpus, the verb to like occurs 97,179 times with a nominal subject and 52,904 times with a direct object; however, the entropy of lemmas of the subjects is 4.33, compared to 9.56 for the direct objects.

subjects of like are more "predictable."


This technique is based on the following assumption: Assumption 3: Sentiment targets related to the topic occur more often in subjective context in the set of relevant documents, than in the background corpus.

while the background corpus contains sentiments towards very diverse subjects, the relevant documents tend to express attitudes related to the topic.


To evaluate the multilanguage-comparability of subjectivity analysis systems, we measure 1) how consistently the system assigns subjectivity labels and 2) how closely numeric scores for systems' confidences correlate with regard to parallel texts in different languages.

we use Cohen's kappa coefficient for the first and Pearson's correlation coefficient for the latter.


We again employed Pearson's correlation metrics to measure the correlations of precision (P), recall (R), and F-measures (F) to kappa (κ) and Pearson's correlation (ρ) values.

we measure the correlations between the sums of P, the sums of R, and the sums of F to κ and ρ for all pairs of systems.


In this paper we restrict the "parts" to words.

we detect errors at the word level for SMT.


When we use b beyond 0.25, the margin becomes less reliable, as the number of positive examples becomes too small.

this causes the SVM parameters we tune on in the first fold to become less applicable to the other folds.


Section 4.3.2) for the TM hits as the level of confidence.

the higher the fuzzy match cost of the TM hit is (lower fuzzy match score), the higher the confidence of recommending the SMT output.


Improvement in Chinese/English NER The result shows that the proposed joint model has a clear win over the initial NER for either Chinese or English NER.

oRG seems to have yielded the greatest gain amongst NE types, which matches our previous observations that the boundaries of Chinese oRG are difficult to identify with the information only coming from the Chinese sentence, while the type of English oRG is uneasy to classify with the information only coming from the English sentence.


Our work on comparator mining is related to the research on entity and relation extraction in information extraction (Cardie, 1997;Califf and Mooney, 1999;Soderland, 1999;Radev et al., 2002;Carreras et al., 2003).

the most relevant work is by Liu (2006a and2006b) on mining comparative sentences and relations.


As a baseline method, we carefully implemented J&L"s method.

cSRs for comparative question identification were learned from the labeled questions, and then a statistical classifier was built by using cSR rules as features.


It has been shown that by abstracting from tool-specific representations of linguistic annotations, annotations from different tagsets can be represented with reference to the OLiA ontologies (and/or with other OWL/RDF-based terminology repositories linked as External Reference Models).

it is possible to compare an existing reference annotation with annotations produced by NLP tools that use independently developed and differently structured annotation schemes (such as Connexor vs. RFTagger vs. Morphisto).


Figure 1: Extract of a document contained in the training data of the first TempEval-1 ually.

the attributes stem, tense and aspect of <EVENT> elements are language specific and needed to be adapted.


Metaphors arise when one concept is viewed in terms of the properties of the other.

it is based on similarity between the concepts.


All of these approaches share the idea of an interconceptual mapping that underlies the production of metaphorical expressions.

metaphor always involves two concepts or conceptual domains: the target (also called topic or tenor in the linguistics literature) and the source (or vehicle).


In this article, we use the explanatory framework of game theory, seeking to rationalize the observed behavior by designing a game that would produce, at equilibrium, the observed dynamics.

we formalize the notion that the price of "locking" the public into a metaphorical frame of reference is that a politician is coerced into staying within the metaphor as well, even if he or she is at the receiving end of a rival's rhetorical move.


While the feature based approach may not be able to fully utilize the syntactic information in a parse tree, an alternative to the feature-based methods, tree kernel methods (Haussler, 1999) have been proposed to implicitly explore features in a high dimensional space by employing a kernel function to calculate the similarity between two objects directly.

the kernel methods could be very effective at reducing the burden of feature engineering for structured objects in NLP research (Culotta and Sorensen, 2004).


In the learning framework, a training or testing instance is formed by a non-overlapping clause(s)/sentence(s) pair.

since implicit relations in PDTB are defined to be local, only clauses from adjacent sentences are paired for implicit cases.


So it is possible to infer a causal relation in Example (8) if and only if 8.b is taken to be the cause event and 8.a is taken to be the effect event.

8.b is taken as happening prior to his going into hospital.


In the paper, we propose a kernel-based method to incorporate the structural information embedded in parse trees.

we directly utilize the syntactic parse tree as a structure feature, and then apply kernels to such a feature, together with other normal features.


If genre labels are organised into a tree (Figure 1), one of the simplest ways to measure distance between two genre labels (= tree nodes) is path length (h(a, b) plen ): where a and b are two nodes in the tree, LCS(a, b) is their Least Common Subsumer, and f (a, LCS(a, b)) is the number of levels passed through when traversing from a to the ancestral node LCS(a, b).

the distance counts the number of edges traversed from nodes a to b in the tree.


To achieve proper normalization in giving weights to misclassified entries, we can redistribute a total weight k − 1 to each row of H proportionally to its values, where k is the number of genres.

given g the row summation of H, we define a weight matrix Q by normalizing the rows of H in a way given by Q ab = (k − 1)h ab /g a , a = b.


The unfiltered version was later reduced to 89,814 question-answer pairs that showed statistical and linguistic properties which made them particularly adequate for our purpose.

trivial, factoid and encyclopedia-answerable questions were removed by applying a series of patterns for the identification of complex questions.


Some pattern examples are the following: We also removed questions that showed statistical values outside of convenient ranges: the number of answers, length of the longest answer and length of the sum of all answers (both absolute and normalized) were taken in consideration.

we discarded questions with the following characteristics: • there were less than three answers 8 • the longest answer was over 400 words (likely a copy-and-paste) • the sum of the length of all answers outside of the (100, 1000) words interval • the average length of answers was outside of the (50, 300) words interval At this point a second version of the dataset was created to evaluate the summarization performance under scoring function (6) and 7; it was generated by manually selecting questions that arouse subjective, human interest from the previous 89,814 question-answer pairs.


We discuss the advantages and disadvantages of various methods, and show that better alignments tend to improve the accuracy of the L2P systems regardless of the actual technique.

one of our proposed methods advances the state of the art in L2P conversion.


Moreover, if a path includes a summary sentence, then candidate sentences on that path are more likely to be selected for summary text.

the similarity of a candidate sentence o m to a summary sentence s n sharing the same path is a measure of strength, indicating how likely o m is to be included in the generated summary (Algorithm 1): Let c om be the path for a given o m .


The first one is the large data condition, based on training data for the NIST 2 2009 evaluation Chinese-to-English track.

all the allowed bilingual corpora except the UN corpus and Hong Kong Hansard corpus have been used for estimating the translation model.


We set out to see whether we could use the HNG method to achieve translation quality improvements by gathering additional translations to add to the training data of the entire LDC language pack, including its dictionary.

we wanted to see if we could achieve translation improvements on top of already state-of-the-art performing systems trained already on the entire LDC corpus.


We used the MTurk web service to gather our annotations.

we first crawled a large set of BBC articles on the internet in Urdu and used this as our unlabeled pool from which to gather annotations.


where Q is the state set, Σ is the alphabet, q 0 is the start state, δ is a deterministic transition function, F and T are the final-state and transition probabilities.

t : Q × Σ → R + and F : Like DFAs, for all w ∈ Σ * , there is at most one state reachable from q 0 .


Q, q 0 , and δ are defined as with DFA product.

be the normalization term; and the numerators of T and F are defined to be the co-emission probabilities (Vidal et al., 2005a), and division by Z ensures that M defines a well-formed probability distribution.


In the same way that missing edges propagate down in DFA representations of SP languages (Lemma 2), the final and transitional probabilities must propagate down in PDFA representations of SP k distributions.

the final and transitional probabilities at states further along paths beginning at the start state must be determined by final and transitional probabilities at earlier states non-increasingly.


As shown, these learners have many desirable properties.

they are globally consistent, locally conservative, and set-driven.


which yield a vector of the same dimensionality), as our notion of compositionality requires models that allow for iterated composition.

this rules out dot product and tensor product.


In order to address the above problem, we propose to consider the translation quality of the English sentences in the summarization process.

the translation quality of each English sentence is predicted by using the SVM regression method, and then the predicted MT quality score of each sentence is incorporated into the sentence evaluation process, and finally both informative and easy-to-translate sentences are selected and translated to form the Chinese summary.


We therefore sample in the direction from source to target.

we visit every tree pair and each of its source nodes i, and update its alignment by selecting between and within two choices: (a) unaligned, (b) aligned with some target node j or .


This allows the system to be ported to any new domain without any manual effort.

it ought to apply to arbitrary Web documents, which are drawn from a huge variety of domains.


Span-HMMs can be used to provide a single categorical value for any span of a sentence using the usual Viterbi algorithm for HMMs.

at test time, we generate a Span-HMM feature for word w j by constructing a Span-HMM that has a span node for the sequence of words between w j and the predicate.


Intuitively, running Baum-Welch over this data means that a span node with state k will be likely to generate two spans t 1 and t 2 if t 1 and t 2 tend to appear in similar contexts.

they should appear between words that are also likely to be generated by the same latent state.


Some examples are the general acquisition of commonsense knowledge (Singh et al., 2002), the use of browser games for that purpose (von Ahn and Dabbish, 2008), and the collaborative annotation of anaphoric reference (Chamberlain et al., 2009).

the use of the Amazon Mechanical Turk, which we use here, has been evaluated and shown to be useful for language processing tasks (Snow et al., 2008).


Each sequence alignment A can be assigned a cost c(A) in the following way: where Σ is the number of gaps in A, n is the number of rows and m the number of sequences.

we sum up the alignment cost for any two symbols from Σ that are aligned with each other, and add the gap cost for each gap.


We want to thank Dustin Smith for the OMICS data, Alexis Palmer for her support with Amazon Mechanical Turk, Nils Bendfeldt for the creation of all web forms and Ines Rehbein for her effort with several parsing experiments.

we thank the anonymous reviewers for their helpful comments.


Each of the T factors in Equation 3 is independent of the instant t, as per the notation in (Rabiner, 1989).

each factor is a function only of the state S i in which the conversation was at time t − 1 and the state S j in which the conversation is at time t, and not of the instants t − 1 or t. It may be expressed as the scalar a ij which forms the ith row and jth column entry of the matrix {a ij } ≡ Θ.


The first is that the direct models considered here, with the exception of Θ M I any , are K-specific.

the number and the identity of conditioning states are both functions of K, for Θ CD and {Θ CI k }; the models may also consist of K distinct submodels, as for {Θ CI k } and {Θ M I k }.


The EDO model yields time-independent transition probabilities which assume conditional inter-participant dependence (cf.

equation 3), where n i ≡ S i and n j ≡ S j , with S yielding the number of participants in in the multiparticipant state S. n i and n j are the numbers of participants simultaneously speaking in states S i and S j , respectively.


we realised the following IP strategies (see examples in Table 1): • SUMMARY of all matching restaurants with or without a User Model (UM), following (Polifroni and Walker, 2008).


by filling slots) but it only changes the generator state.

the NLG user simulation tells us what the user is most likely to do next, if we were to stop generating now.


However, change to {1,2} entails crossing the hyperplanes λ 11 =λ 12 and λ 2 =λ 12 , and is thus now frequency dependent.

consider a system at a stable FP (γ, 1), for some N/V pair.


These edit distances define a condi-tional exponential family distribution when conditioned on an ancestral word.

for any fixed w a : where align(w a , w d ) is the set of possible alignments between the phonemes in words w a and w d .


We are seeking the maximum likelihood estimate of each ϕ, given fixed alignments π: To find this maximizer for any given π , we need to find a marginal distribution over the edges connecting any two languages a and d. With this distribution, we calculate the expected "alignment unigrams."

for each pair of phonemes x and y (or empty phoneme ε), we need to find the quantity: where we denote #(x, y; z) to be the number of times the pair of phonemes (x, y) are aligned in alignment z.


Intuitively, composition creates a new transducer that takes the output from the first transducer, processes it through the second transducer, and then returns the output of the second transducer.


In our setting, however, n-best lists are problematic; early experiments showed that a 10,000-best list for a typical message only accounts for 50% of message log perplexity.

the posterior marginals in our model are (at least initially) fairly flat.


5 Instead, we propose to minimize the KL divergence between the "expected" marginal distribution and the approximated "expected" marginal distribution: where τ is a term acting as a surrogate for the posterior distribution over w without the information from µ.

we seek to approximate µ not on its own, but as it functions in an environment representing its final context.


The procedure for calculating these statistics is described in Li and Eisner (2009), which amounts to using an expectation semiring (Eisner, 2001) to compute expected transitions in τ •μ * under the probability distribution τ • µ.

from there, we need to create the automaton we need to divide out the influence of τ (w).


Finally, λ can be computed by ensuring that the approximate and exact expected marginals have the same partition function.

with the other parameters fixed, solve: which amounts to rescalingμ by some constant.


Enqueued Expanded Zeros Accuracy Word Error Rate  1  2:03:06  964  964  44157  100%  0%  2  0:13:00  132  132  5197  100%  0%  3  0:05:42  91  91  3080  100%  0%  4 Unfinished  As far as the running time of the algorithm goes, we see a substantial variance: from a few minutes to several hours for most of the longer ciphers, and that there are some that take longer than the threshold we gave in the experiment.

there is substantial variability in the the running times seen.


The results, however, do not show that running time monotonically decreases with length.

the length 8500 cipher generates more solutions than the length 3500 or 6000 ones.


For each character u, we block sample the entire set {λ (u,h) } h , and likewise for each character h. Many of the steps detailed above involve the consideration of all possible edit-sequences consistent with (i) a particular undeciphered word u i and (ii) the entire lexicon of words in the known language (or some subset of words with a particular part-of-speech).

we need to both sample from and sum over this space of possibilities repeatedly.


Those variables are syntactic and represent only themselves.


The tree transformation computed by M is the relation where ⇒ * is the reflexive, transitive closure of ⇒.

the tree t can be transformed into u if there exists an initial state q such that we can derive u from q(t) in several derivation steps.


XTT are a simple, natural model for tree transformations, however they are not suitably expressive for all applications in machine translation (Shieber, 2007).

all tree transformations of XTT have a certain locality condition, which yields that the input tree and its corresponding translation cannot be separated by an unbounded distance.


However, our formalism is more flexible and our algorithm more practical.

we make the following contributions: • theoretically, we show that for a large class of modern shift-reduce parsers, dynamic programming is in fact possible and runs in polynomial time as long as the feature functions are bounded and monotonic (which almost always holds in practice); • practically, dynamic programming is up to five times faster (with the same accuracy) as conventional beam-search on top of a stateof-the-art shift-reduce dependency parser; • as a by-product, dynamic programming can output a forest encoding exponentially many trees, out of which we can draw better and longer k-best lists than beam search can; • finally, better and faster search also leads to better and faster learning.


While it may seem that such grammars will be overly large, they are in fact reasonably compact, being linear in the treebank size B, while G is exponential in the length of a sentence.

we found that G I was smaller than explicit extraction of all depth 1 and 2 unbinarized fragments for our !


Even extracting binarized fragments (depth 1 and 2, with one order of parent annotation) gives us 0.75 million rules, and, practically, we would need fragments of greater depth.

g. each derivation d in g has a nonempty set of corresponding derivations {d I } = π −1 (d) in g I , because fragments f in d correspond to multiple fragments f I in g I that differ only in their indexed symbols (one f I per occurrence of f in B).


However, we now consider a usefully broad class of weighting schemes for which the posterior probabilities under G of derivations d are preserved in G I .

assume that we have a weighting ω on rules in G I which does not depend on the specific indices used.


Our model can incorporate both size and lexical properties.

we set ω CONT (r) for each binary CON-TINUE rule r to a learned constant ω BODY , and we set the weight for each rule with a POS parent to a 6 Intermediate symbols are those created during binarization.


Petrov and Klein (2007) propose a multistage coarse-to-fine method in which they construct a sequence of increasingly refined grammars, reparsing with each refinement.

in their approach, which we adopt here, coarse-to-fine pruning is used to quickly compute approximate marginals, which are then used to prune subsequent search.


Due to the difficulty in deep semantic parsing, most previous work focuses on shallow semantic parsing, which assigns a simple structure (such as WHO did WHAT to WHOM, WHEN, WHERE, WHY, HOW) to each predicate in a sentence.

the well-defined semantic role labeling (SRL) task has been drawing increasing attention in recent years due to its importance in natural language processing (NLP) applications, such as question answering (Narayanan and Harabagiu, 2004), information extraction (Surdeanu et al., 2003), and co-reference resolution (Kong et al., 2009).


There have been other efforts in NLP on joint learning with various degrees of success.

the recent shared tasks of CoNLL 2008(Surdeanu et al., 2008Hajic et al., 2009) tackled joint parsing of syntactic and semantic dependencies.


For nominal predicates, a binary classifier is trained to predict whether a noun is a nominal predicate or not.

any word POS-tagged as NN is considered as a predicate candidate in both training and testing processes.


Table 1: Feature set for nominal predicate recognition Our Chinese SRL models for both verbal and nominal predicates adopt the widely-used SRL framework, which divides the task into three sequential sub-tasks: argument pruning, argument identification, and argument classification.


Similar to most of the state-of-the-art systems (Pradhan et al., 2005;Xue, 2008;Li et al., 2009), the top-best parse tree is first returned from our syntactic parser and then fed into the SRL system.

the verbal (nominal) SRL labeler is in charge of verbal (nominal) predicates, respectively.


Here, the parameter α is a balance factor indicating the importance of the semantic parsing model.

(F*, t*) with maximal Score(F, t|x) is selected as the final syntactic and semantic parsing results.


It is worth pointing out that all features (Xue, 2008;Li et al., 2009) used in our SRL model can be instantiated and their values are same as the ones when the whole tree is available.

the probability computed from the SRL model is interpolated with that of the syntactic parsing model in a log-linear way (with equal weights in our experiments).


In Table  2, feature sf4 gives the possible core semantic roles that the focus predicate may take, according to its frame file; feature sf5 presents the semantic roles that the focus predicate has already occupied; feature sf6 indicates the semantic roles that the focus predicate is expecting; and SF1-SF8 are combined features.

if the current constituent covers n predicates, then 14 * n features would be instantiated.


(2009), 648 files (chtb 081 to 899.fid) are selected as the training data, 72 files (chtb 001 to 040.fid and chtb 900 to 931.fid) are held out as the test data, and 40 files (chtb 041 to 080.fid) are selected as the development data.

the training, test, and development data contain 31,361 (8,642), 3,599 (1,124), and 2,060 (731) verbal (nominal) propositions, respectively.


2 Throughout the experiments linear classifiers are employed; they are trained by minimizing Equation (1), using a stochastic gradient descent (SGD) algorithm.

the learning rate schedule from PEGASOS is adopted (Shalev-Shwartz et al., 2007), and the modified Huber loss, introduced by Zhang (2004), is chosen as loss function L. 3 SGD receives two hyperparameters as input: the number of iterations T , and the regularization parameter λ.


In this paper, we propose a way to incorporate a bilingual dictionary into a probabilistic topic model so that we can apply topic models to extract shared latent topics in text data of different languages.

we propose a new topic model called Probabilistic Cross-Lingual Latent Semantic Analysis (PCLSA) which extends the Probabilistic Latent Semantic Analysis (PLSA) model by regularizing its likelihood function with soft constraints defined based on a bilingual dictionary.


Definition 2 (Cross-Lingual Topic): A crosslingual topic θ is a semantically coherent multinomial distribution over all the words in the vocabularies of languages L 1 , ..., L s .

p(w|θ) would give the probability of a word w which can be in any of the s languages under consideration.


Our PCLSA model assumes the availability of bi-lingual dictionaries for at least some language pairs, which are generally available for major language pairs.

for text data in languages L 1 , ..., L s , if we represent each language as a node in a graph and connect those language pairs for which we have a bilingual dictionary, the minimum requirement is that the whole graph is connected.


Thus when a cross-lingual topic picks up words that cooccur in mono-lingual text, it would prefer picking up word pairs whose translations in other languages also co-occur with each other, giving us a coherent multilingual word distribution that characterizes well the content of text in different languages.

let Θ = {θ j } (j = 1, ..., k) be a set of k cross-lingual topic models to be discovered from a multilingual text data set with s languages such that p(w|θ i ) is the probability of word w according to the topic model θ i .


When λ = 0, we recover the standard PLSA.

we will search for a set of values for all our parameters that can maximize the objective function defined above.


This optimization problem can be solved using a Generalized Expectation-Maximization (GEM) algorithm as described in (Mei et al., 2008a).

in the E-step of the algorithm, the distribution of hidden variables is computed using Eq.


Since PLSA takes advantage of the word co-occurrences in the document level to find semantic topics, directly using it for a multilingual corpus will result in finding topics mainly reflecting a single language (because words in different languages would not co-occur in the same document in general).

the discovered topics are mostly monolingual.


For the third data set, (English 3, Chinese 3), the Chinese corpus is the same as in data set one, but the English corpus is chosen from 2001.10.02 to 2001.10.07 which contains 1530 documents.

in the first data set,  the English corpus and Chinese corpus are comparable with each other, because they cover similar events during the same period.


Model III calculates the probability of a sense given a context according to the component words of the sense paraphrase.

it chooses the sense type which maximizes the probability (given the context) of the paraphrase component word with the highest likelihood of occurring in that context.


This paper establishes a theoretical connection between two very different kinds of probabilistic models: Probabilistic Context-Free Grammars (PCFGs) and a class of models known as Latent Dirichlet Allocation (Blei et al., 2003;Griffiths and Steyvers, 2004) models that have been used for a variety of tasks in machine learning.

we show that an LDA model can be expressed as a certain kind of PCFG, so Bayesian inference for PCFGs can be used to learn LDA topic models as well.


If X ∈ W (i.e., if X is a terminal) then T X = {X}, i.e., T X consists of a single tree, which in turn only consists of a single node labelled X.

if X ∈ N (i.e., if X is a nonterminal) then where R A = {A → β : A → β ∈ R} for each A ∈ N , and TREE X (T B 1 , .


If X ∈ W (i.e., if X is a terminal) then G X is the distribution that puts probability 1 on the single-node tree labelled X.

if X ∈ N (i.e., if X is a nonterminal) then: where: TD A (G 1 , .


We can use non-uniform sparse Dirichlet priors on the probabilities of these rules to encourage "topic stickiness".

by setting the Dirichlet parameters for the "topic shift" rules Doc j,i → Doc j,i Topic i where i = i much lower than the parameters for the "topic preservation" rules Doc j,i → Doc j,i Topic i , Bayesian inference will be biased to find distributions in which adjacent words will tend to have the same topic.


In this paper, we present another rational model of eye movement control in reading that, like Reichle and Laurent, makes predictions for fixation durations and locations, but which focuses instead on the dynamics of word identification at the core of the task of reading.

our model identifies the words in a sentence by performing Bayesian inference combining noisy input from a realistic visual model with a language model that takes context into account.


The most recent version, E-Z Reader 10 (Reichle, Warren, & McConnell, 2009), has a new component that can produce longer between-word regressions.

the model includes a flag for postlexical integration failure, that -when triggered -will instruct the model to produce a between-word regression to the site of the failure.


Specifically, the model includes a flag for postlexical integration failure, that -when triggered -will instruct the model to produce a between-word regression to the site of the failure.

between-word regressions in E-Z Reader 10 can arise because of postlexical processes external to the model's main task of word identification.


At its core, the framework we are proposing is one of reading as Bayesian inference.

the model begins reading with a prior distribution over possible identities of a sentence given by its language model.


Visual information about each character is obtained by sampling.

we represent each letter as a 26-dimensional vector, where a single element is 1 and the other 25 are zeros, and given this representation, foveal input for a letter is given as a sample from a 26-dimensional Gaussian with a mean equal to the letter's true identity and a diagonal covariance matrix Σ(ε) = λ (ε) −1/2 I.


To perform belief update given a new visual input, we create a new wFSA to represent the likelihood of each character from the sample.

this wFSA has only a single chain of states, where, e.g., the first and second state in the chain are connected by 27 (or fewer) arcs, which emit each of the possible characters for w 1 along with their respective likelihoods given the visual input (as in the inner term of Equation 3).


We implemented our model with wFSAs using the OpenFST library (Allauzen, Riley, Schalkwyk, Skut, & Mohri, 2007).

we constructed the model's initial belief state (i.e., the distribution over sentences given by its language model) by directly translating the bigram model into a wFSA in the log semiring.


In Simulation 2, we perform a more direct test of the idea that making regressions is a rational response to the problem of confidence falling about previous regions using optimization techniques.

we search for optimal policy parameter values (α, β ) for three different measures of performance, each representing a different tradeoff between the importance of accuracy and speed.


Finally, there was a statisically significant interaction between syntax and discourse whereby context decreases reading times much more when a garden path is present compared to an unambiguous structure.

a supportive context helped reduce the effect of a garden path.


We represent T and H as syntactic trees, as common in the RTE literature (Zanzotto et al., 2009;Agichtein et al., 2008).

we assume MINIPAR-style (Lin, 1993) dependency trees where nodes represent text expressions and edges represent the syntactic relations between them.


We propose using the entailment graph structure, which describes entailment relations between predicates, to naturally present propositions hierarchically.

the entailment hierarchy can be used as an additional facet, which can improve navigation and provide a compact hierarchical summary of the propositions.


Then for every short text, either a question or an answer, a 1,500-dimensional vector can be generated.

all the features we have adopted are binary, for they only have to denote whether the corresponding word appears in the text or not.


(2009) present a general framework for generating text descriptions of image and video content based on image parsing.

images are hierarchically decomposed into their constituent visual patterns which are subsequently converted into a semantic representation using WordNet.


Given an image I, and a related knowledge database κ, create a natural language description C which captures the main content of the image under κ.

in the news story scenario, we will generate a caption C for an image I and its accompanying document D. The training data thus consists of document-image-caption tu- A Nasa satellite has documented startling changes in Arctic sea ice cover between 2004 and 2005.


The model is based on the assumption that images and their surrounding text are generated by mixtures of latent topics which are inferred from a concatenated representation of words and visual features.

images are preprocessed so that they are represented by word-like units.


At the same time we modify our surface realization component so that it takes note of the image annotation probabilities.

we use an adaptive language model (Kneser et al., 1997) that modifies an n-gram model with local unigram probabilities: •P(len(C) = n) where P(w i ∈ C|w i ∈ D) is the probability of w i appearing in the caption given that it appears in the document D, and P adap (w i |w i−1 , w i−2 ) the language model adapted with probabilities from our image annotation model: where P back (w|h) is the probability of w given the history h of preceding words (i.e., the original trigram model), P adap (w) the probability of w according to the image annotation model, P back (w) the probability of w according to the original model, and β a scaling parameter.


To avoid these problems, we turn our attention to phrases which are naturally associated with function words and can potentially capture long-range dependencies.

we obtain phrases from the output of a dependency parser.


In these data sets, coreference relations are defined as a limited version of a typical coreference; this generally means that only the relations where expressions refer to the same named entities are addressed, because it makes the coreference resolution task more information extraction-oriented.

the coreference task as defined by MUC and ACE is geared toward only identifying coreference relations anchored to an entity within the text.


The results typically show that the former achieved better performance than the latter.

it indicates that exploiting the action history features are significantly useful for reference resolution in this data set.


We do this by integrating the partial environment model into the policy function.

we modify the log-linear policy p(a|s; q, θ) by adding lookahead features φ(s, a, q) which complement the local features used in the previous model.


Therefore we incorporate them as separate features in the model, and allow the learning process to estimate their weights.

we select actions a based on the current state s and the partial environment model q, resulting in the following policy definition: φ(s,a ,q) , where the feature representation φ(s, a, q) has been extended to be a function of q.


We apply our algorithm to the task of interpreting help documents to perform software related tasks Kushman et al., 2009).

we consider documents from Microsoft's Help and Support website.


Our work differs from theirs primarily in the task (syntax vs. phonology) and the variables governed by the phylogeny: in our model it is the grammar parameters that drift (in the prior) rather than individual word forms (in the likelihood model).

we consider dependency induction in the DMV model of Klein and Manning (2004).


Second, while the choice of diagonal covariance is efficient, it causes components of θ that correspond to features occurring in only one language to be marginally independent of the parameters of all other languages.

only features which fire in more than one language are coupled by the prior.


As pointed out in Finkel and Manning (2009), there is a simple equivalence between hierarchical regularization as described here and the addition of new tied features in a "flat" model with zero-meaned Gaussian regularization on all parameters.

instead of parameterizing the objective in Section 2.4 in terms of multiple sets of weights, one at each node in the phylogeny (the hierarchical parameterization, described in Section 2.4), it is equivalent to parameterize this same objective in terms of a single set of weights on a larger of group features (the flat parameterization).


For example, for some settings of σ 2 , but not others, the model would learn that determiners head noun phrases.

we observed that even when direct accuracy did fluctuate, undirected accuracy remained more stable.


In our work, we rely on a formal notion of textual definition.


The matching score S a,b is calculated on the generalized sentences s k of s k and s j of s j as follows: where ω k a and ω j b are the a-th and b-th word classes of s k and s j , respectively.

the matching score equals 1 if the a-th and the b-th tokens of the two original sentences have the same word class.


In this paper, we show that enforcing the ontological distinctions between the different types of part-whole relations enable information extraction systems to capture a wider variety of both generic and specialised part-whole lexico-syntactic patterns and tuples.


learning the individual types of part-whole relations?

we determine whether the performance of state-of-the-art IE systems in learning the individual part-whole relation types increases (due to more coherency in the relations' linguistic realizations) or drops (due to fewer examples), compared to the traditional practice of considering a single partwhole relation.


Are the patterns and tuples discovered when focusing on a specific part-whole relation type confined to that particular type?

we investgate whether IE systems discover examples representative of the different types by targetting one particular part-whole relation type.


Are more distinct examples discovered when IE systems learn the individual part-whole relation types?

we determine whether a wider variety of unique patterns and tuples are extracted when IE systems target the different types of part-whole relations instead of considering a single part-whole relation that subsumes all the different types.


The same parameters as Pantel and Pennacchiotti (2006) were used.

the 10 most reliable patterns inferred from the initial seeds are bootstrapped to induce 100 part-whole tuples.


In this work, we go one step further and study the semantic structure of a query, i.e., individual constituents of a query and their semantic roles.


A second contribution of this work is to present methods that automatically extract the semantic structure of noun phrase queries, i.e., IHs, IMs and the semantic labels of IMs.

we investigate the use of transition, lexical, semantic and syntactic features.


For example, the query "california national" and "national parks califorina" both imply the question "What are the national parks in California?".

a number of works investigated the importance of head noun extraction in understanding what-type questions (Metzler and Croft, 2005;Li et al., 2008a).


Furthermore, for each IM segment, we would like to assign a semantic label, denoted by IM:a, a ∈ A c , indicating which attribute name it refers to.

our label set consists of Y = {IH, {IM:a} a∈Ac , Other}.


We can also generalize this type of features from words to n-grams.

instead of inspecting the word identity at the current position, we inspect the n-gram identity by applying a window of length n centered at the current position.


The first metric is computed based on precision and recall at the segment level.

let us assume that the true segment sequence of a query is s = (s 1 , s 2 , .


However, a natural extension to the method which comes to mind, is using multiple assisting languages.

combining the evidence from all the feedback models of more than one assisting language, to get a feedback model which is better than that obtained using a single assisting language.


This survey paper aims to complement, rather than supersede, these previously published materials.

while existing survey papers discuss learning-based coreference research primarily in the context of the influential mention-pair model, we additionally survey recently proposed learning-based coreference models, which attempt to address the weaknesses of the mention-pair model.


Another issue that surrounds the acquisition of the mention-pair model concerns the way training instances are created.

to determine whether a pair of NPs is coreferent or not, the mention-pair model needs to be trained on a data set where each instance represents two NPs and possesses a class value that indicates whether the two NPs are coreferent.


One criticism of the closest-first and best-first clustering algorithms is that they are too greedy.

clusters are formed based on a small subset of the pairwise decisions made by the model.


The search starts at the root, and a partitioning of the NPs is incrementally constructed as we move down the tree.

based on the coreference decisions it has made in the first i−1 levels of the tree, the algorithm determines at the ith level whether the ith NP should start a new cluster, or to which preceding cluster it should be assigned.


Since the classification model is trained independently of the clustering algorithm, improvements in classification accuracy do not guarantee corresponding improvements in clustering-level accuracy.

overall performance on the coreference task might not improve.


First, since each candidate antecedent for an anaphoric NP to be resolved is considered independently of the others, the model only determines how good a candidate antecedent is relative to the anaphoric NP, but not how good a candidate antecedent is relative to other candidates.

it fails to answer the question of which candidate antecedent is most probable.


This kind of error arises in part because the later coreference decisions are not dependent on the earlier ones.

had the model taken into consideration that "Mr. Clinton" and "Clinton" were in the same cluster, it probably would not have posited that "she" and "Clinton" are coreferent.


This is not an easy question, as researchers have been evaluating their resolvers on different corpora using different evaluation metrics and preprocessing tools.

preprocessing tools can have a large impact on the performance of a resolver (Barbu and Mitkov, 2001).


In this corpus, we organize documents that describe the same seminal event into topics.

the topics shown in this example describe the seminal event of buying ATI by AMD (topic 43) and the seminal event of buying EDS by HP (topic 44).


This organization of event mentions leads to the idea of creating an event hierarchy which has on the first level, event mentions, on the second level, individuated events, and on the third level, generic events.

the event hierarchy corresponding to the event mentions annotated in our example is illustrated in Figure 1.